Adaptive Route Planning for Autonomous Logistics Robots in Dynamic Warehouse Environments

Abstract

The enormous increases in global retail has pushed automation levels to new extremes, with autonomous mobile robots (AMRs) increasingly becoming a centerpiece of modern logistics infrastructure. One of the major challenges faced by traditional multiagorithm design and line models, due to static routing charts or offline algorithmic updates, when it has to be deployed in highly dynamic unpredectable working environment within an intra-logistics setting. This paper offers a resilient, adaptive routing framework for the well-timed delivery of independent logistics robots over uniquely temporary traffic and sudden tangible obstructions. Synthesizing localized real-time sensory perception and distributed topological map updates, the architecture dynamically re-calculates optimal travel trajectories making systemic deadlocks impossible and minimizing idle times drastically. Results from computational evaluations across simulated warehouse layouts with varying spatial complexity show that the adaptive framework improves fleet-wide operational efficiency (by up to 24.3%) in comparison to conventional fixed-path planning configurations and simultaneously leads to lower total energy expenditure. Together these results provide evidence of clinical feasibility for inclusion of decentralized, reactive real-time routing models into heavy-duty industrial automation applications.